Dexbotic extends Vision-Language-Action (VLA) models with SimpleVLA-RL algorithm for RL post-training. ## Installation ### 🐳 Docker (Recommended) We strongly recommend using Docker as a unified, consistent, and reproducible environment for training and deployment. This approach not only ensures reliability across workflows but also minimizes potential issues arising from CUDA version differences and Python dependency conflicts. > See [`dockerfile/Dockerfile.RL`](dockerfile/Dockerfile.RL) for more details. 0. Prerequisites + Ubuntu 20.04 or 22.04 + NVIDIA GPU: RTX H20 (8 GPUs recommended for training; 1 GPU for deployment) + NVIDIA Docker installed 1. Step 1: Clone the Repository ```bash git clone git@gitlab.dexmal.com:robotics/dexbotic.git ``` 2. Step 2: Start Docker ```bash docker run -it --rm --gpus all \ -v /path/to/dexbotic:/dexbotic \ dexmal/dexbotic:rl \ bash ``` 3. Step 3: Activate Dexbotic Environment ```bash cd /dexbotic conda activate dexbotic-rl pip install -e . ``` ## Launch RL Post-Training ```bash deepspeed playground/benchmarks/libero/libero_simplevla_rl.py \ --task=train \ --sft_model_path=/path/to/sft-checkpoint \ --dataset_name=libero_10 ``` > **Note:** The rollout process in RL post-training may take **some** time to collect enough trajectories for per-step updates. Please be patient.